Learnability with PAC Semantics for Multi-agent Beliefs

This work proposes a new technical foundation for demonstrating Probably Approximately Correct (PAC) learning with multiagent epistemic logics, using implicit learning to incorporate observations into the background knowledge. We explore the sample complexity and the circumstances in which the algorithm can be made efficient.